Abstract :
Keywords Electroencephalography (EEG); Psychopathology; Adolescence; Clustering; Bayesian statistics Highlights * Flexible analysis pipeline identifies EEG-based clusters of individuals. * Clusters of 12-year-olds differentiated by resting state EEG characteristics. * Novel evidence on empirical, data-driven neurophysiological subgroups. * Bayesian models find differences in distress, sleep and cognition between clusters. * Potential applications for risk prediction and early intervention in adolescence. Abstract Introduction To better understand the relationships between neurophysiology, cognitive function and psychopathology risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health between such subgroups. Methods We developed a flexible and scaleable multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. For this preliminary cross-sectional study, EEG data was collected from 59 individuals in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. Results We identified 5 core clusters associated with distinct subtypes of resting state EEG frequency content. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. Conclusion This method provides the potential to identify neurophysiological subgroups of adolescents in the general population based on resting state EEG, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development. Author Affiliation: (a) School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia (b) ARC Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), Brisbane, QLD, Australia (c) Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, Australia (d) School of Information Science and Engineering, Yunnan University, Kunming 650500, China * Correspondence to: Level 8, Y Block, QUT Gardens Point Campus, 2 George St, Brisbane 4000, QLD, Australia. Article History: Received 12 November 2021; Revised 27 June 2022; Accepted 26 July 2022 Byline: Owen Forbes [owen.forbes@hdr.qut.edu.au] (a,b,*), Paul E. Schwenn (c), Paul Pao-Yen Wu (a,b), Edgar Santos-Fernandez (a,b), Hong-Bo Xie (a,b,d), Jim Lagopoulos (c), Larisa T. McLoughlin (c), Dashiell D. Sacks (c), Kerrie Mengersen (a,b), Daniel F. Hermens (c)